Funding

We are primarily funded by the National Institutes of Health (NIH). Below is a sample of our active research grants. An overview of our work supported by these grants can be found on our research page.

The goal of this research program is to develop methods for incorporating expert knowledge about functional genomics annotations from sources such as ENCODE into the genetic analysis of complex diseases. Do annotations help identify genetic risk factors? What is the best way to incorporate knowledge? The project is a collaboration with Dr. Folkert Asselbergs from the University of Utrecht and Dr. Scott Williams from Case-Western Reserve University.

The goal of this research program is to develop novel machine learning methods for incorporating rare genetic variants into gene-gene interaction analyses. We are developing new feature engineering strategies to convert sets of rare variants to common features that can be combined with single-nucleotide polymoprhisms (SNPs) and other biomarkers for the prediction of disease susceptibility. We will apply these methods to the genetic analysis of infectious diseases including HIV/AIDS.

This is a new research program focused on developing multi-objective optimization strategies for developing genomic models that can be used for precision medicine. A central focus is on developing new methods for Pareto optimization. These methods will be applied to the genomic analysis of breast and lung cancer. This project is a collaboration with Dr. David Beer from the University of Michigan and Dr. Xiuzhen Huang from Arkansas State University.

Integrative Big Data for Biomedical Discovery (PADOH, Co-I – Moore)

The goal of this state-funded project is to develop new biomedical computing and informatics strategies for the study of opioid abuse in Pennsylvania. A focus is on the analysis of electronic health record (EHR) data using methods such as deep learning.

The goal of this project is to establish a research program at Penn to study the genetics, genomics, immunology, and physiology of type I diabetes in pancreatic tissue from organ donors. We are building a graph database to integrate the diverse data types and will make available cutting edge artificial intelligence and data visualization methods for the discovery of new patterns related to initiation and progression of diabetes. All data are made publicly available.

We lead the Exposure Biology Informatics Core for this center. The goal of the core is provide cutting edge bioinformatics support for studying the impact of environmental exposures on human health. A focus is on providing next-generation sequencing analysis support. We are also providing artificial intelligence and data visualization methods and resources to investigators.

We lead the Biomedical Informatics program for this translational research center. Our goal is provide comprehensive support for clinical data access, integration, and analysis. We also provide research computing infrastructure as well as education and training opportunities. For example, we are providing artificial intelligence and data visualization methods and resources to investigators.